97d75f47274b8d7e0e5b235346149da0bae9fc79,plot_adhd_covariance2.py,,,#,84

Before Change

    emp_covs, n_samples = zip(*data)

    // Normalize covariance matrices
    for cov, n in zip(emp_covs, n_samples):
        cov /= n
        np.testing.assert_almost_equal(np.diag(cov), np.ones(cov.shape[0]))

    print("-- Computing covariance matrices ...")
    from nilearn.honorio_samaras import honorio_samaras
    est_precs, all_crit = honorio_samaras(emp_covs, rho, n_samples,
                                          normalize_n_samples=True,

After Change

        data.append(mem.cache(covariance_matrix)(n))
    emp_covs, n_samples = zip(*data)

    print("-- Computing precision matrices ...")
    from nilearn.group_sparse_covariance import group_sparse_covariance
    est_precs, all_crit = group_sparse_covariance(emp_covs, rho, n_samples,
                                          normalize_n_samples=True,
                                          n_iter=5,

Instances